Knowledge within a given domain may be represented in many ways. One form of knowledge representation may comprise a list representing all available values for a given subject. For example, knowledge in the area of “human body tissue types” may be represented by a list including “hepatic tissue,” “muscle tissue,” “epithelial tissue,” and many others. To represent the total knowledge in a given domain, a number of lists may be needed. For instance, one list may be needed for each subject contained in a domain. Lists may be useful for some applications, however, they generally lack the ability to define relationships between the terms comprising the lists. Moreover, the further division and subdivision of subjects in a given domain typically results in the generation of additional lists, which often include repeated terms, and which do not provide comprehensive representation of concepts as a whole.
Some lists, such as structured lists, for example, may enable computer-implemented keyword searching. The shallow information store often contained in list-formatted knowledge, however, may lead to searches that return incomplete representations of a concept in a given domain.
An additional method of representing knowledge is through thesauri. Thesauri are similar to lists, but they further include synonyms provided alongside each list entry. Synonyms may be useful for improving the recall of a search by returning results for related terms not specifically provided in a query. Thesauri still fail, however, to provide information regarding relationships between terms in a given domain.
Taxonomies build on thesauri by adding an additional level of relationships to a collection of terms. For example, taxonomies provide parent-child relationships between terms. “Anorexia is-a eating disorder” is an example of a parent-child relationship via the “is-a” relationship form. Other parent-child relationship forms, such as “is-a-part-of” or “contains,” may be used in a taxonomy. The parent-child relationships of taxonomies may be useful for improving the precision of a search by removing false positive search results. Unfortunately, exploring only hierarchical parent-child relationships may limit the type and depth of information that may be conveyed using a taxonomy. Accordingly, the use of lists, thesauri, and taxonomies present drawbacks for those attempting to explore and utilize knowledge organized in these traditional formats.
Additional drawbacks may be encountered when searches of electronic data sources are conducted. As an example, searches of electronic data sources typically return a voluminous amount of results, many of which tend to be only marginally relevant to the specific problem or subject being investigated. Researchers or other individuals are then often forced to spend valuable time sorting through a multitude of search results to find the most relevant results. It is estimated, for example, that scientists spend 20% of their time searching for information existing in a particular area. This is time that highly-trained investigative researchers must spend simply uncovering background knowledge. Furthermore, when an electronic search is conducted, data sources containing highly relevant information may not be returned to a researcher because the concept sought by the researcher is identified by a different set of terms in the relevant data source. This may lead to an incomplete representation of the knowledge in a given subject area. These and other drawbacks exist.